# MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding

arXiv: https://arxiv.org/abs/2604.00513 · HTML: https://mlsanity.com/paper/2604.00513/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 31 of its 61 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 30 of 31)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking](https://mlsanity.com/paper/2601.04720/) (2026)
- [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://mlsanity.com/paper/2410.05160/) (2024)
- [SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models](https://mlsanity.com/paper/2504.11468/) (2025)
- [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](https://mlsanity.com/paper/2412.16855/) (2024)
- [MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs](https://mlsanity.com/paper/2411.02571/) (2024)
- [E5-V: Universal Embeddings with Multimodal Large Language Models](https://mlsanity.com/paper/2407.12580/) (2024)
- [UFT: Unifying Supervised and Reinforcement Fine-Tuning](https://mlsanity.com/paper/2505.16984/) (2025)
- [Think Then Embed: Generative Context Improves Multimodal Embedding](https://mlsanity.com/paper/2510.05014/) (2025)
- [Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings](https://mlsanity.com/paper/2602.13823/) (2026)
- [When Language Overrules: Revealing Text Dominance in Multimodal Large Language Models](https://mlsanity.com/paper/2508.10552/) (2025)
- [O1 Embedder: Let Retrievers Think Before Action](https://mlsanity.com/paper/2502.07555/) (2025)
- [UniECS: Unified Multimodal E-Commerce Search Framework with Gated Cross-modal Fusion](https://mlsanity.com/paper/2508.13843/) (2025)
- [Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigm](https://mlsanity.com/paper/2510.14321/) (2025)
- [Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models](https://mlsanity.com/paper/2602.02244/) (2026)
- [MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding](https://mlsanity.com/paper/2508.11999/) (2025)
- [Reason to Contrast: A Cascaded Multimodal Retrieval Framework](https://mlsanity.com/paper/2602.23369/) (2025)
- [TRACE: Task-Adaptive Reasoning and Representation Learning for Universal Multimodal Retrieval](https://mlsanity.com/paper/2603.02929/) (2026)
- [Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data](https://mlsanity.com/paper/2410.17337/) (2024)
- [EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-commerce Models](https://mlsanity.com/paper/2508.15721/) (2025)
- [MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding](https://mlsanity.com/paper/2511.12449/) (2025)
- [MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling](https://mlsanity.com/paper/2502.00321/) (2025)
- [Adaptive Global and Fine-Grained Perceptual Fusion for MLLM Embeddings Compatible with Hard Negative Amplification](https://mlsanity.com/paper/2602.05729/) (2026)

## Built on by (most cited first, 4 of 4)

- [Douyin Multimodal Embedding Model Technical Report](https://mlsanity.com/paper/2608.02148/) (2026)
- [TLive-Omni: An Omni-Modal Understanding Model for E-Commerce Live Streaming](https://mlsanity.com/paper/2608.20958/) (2026)
- [Traceable LLM Reasoning for Fake-Order Fraud Detection](https://mlsanity.com/paper/2607.23075/) (2026)
- [PailitaoGR: Latent Think-with-Images for Generative Image Retrieval](https://mlsanity.com/paper/2608.26658/) (2026)

## Abstract (arXiv)

With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Although recent multimodal large language models (MLLMs) have driven significant progress in product understanding, they are typically employed as feature extractors that implicitly encode product information into global embeddings, thereby limiting their ability to capture fine-grained attributes. Therefore, we argue that leveraging the reasoning capabilities of MLLMs to explicitly model fine-grained product attributes holds significant potential. Nevertheless, achieving this goal remains non-trivial due to several key challenges: (i) long-context reasoning tends to dilute the model's attention to salient information in the raw input; (ii) supervised fine-tuning (SFT) primarily encourages rigid imitation, limiting the exploration of effective reasoning strategies; and (iii) fine-grained details are progressively attenuated during forward propagation. To address these issues, we propose MOON3.0, the first reasoning-aware MLLM-based model for product representation learning. Our method (1) employs a multi-head modality fusion module to adaptively integrate raw signals; (2) incorporates a joint contrastive and reinforcement learning framework to autonomously explore more effective reasoning strategies; and (3) introduces a fine-grained residual enhancement module to progressively preserve local details throughout the network. Additionally, we release a large-scale multimodal e-commerce benchmark MBE3.0. Experimentally, our model demonstrates state-of-the-art zero-shot performance across various downstream tasks on both our benchmark and public datasets.